ALLSTaR - Automated LLM-Driven Scheduler Generation and Testing for Intent-Based RAN

#AI #ML #LLM #6G #RAN #ORAN #dApps #AIRAN #intelligentRAN #intentbased #intentdriven #schedulers #IBN #cellular #wireless #networking #communication #futurenetworks #ieee

Special Presentation by Dr. Maxime Elkael (Northeastern U., USA)

Hosted by the Future Networks Artificial Intelligence & Machine Learning (AIML) Working Group

Date/Time: Thursday, 18 September 2025 @ 12:00 UTC (12 PM GMT)

PDH Certificate: while basic attendance is free, this course also offers one (1) Professional Development Hour (PDH) for a nominal fee; please choose the appropriate "Registration Fee" when registering; additional terms and conditions apply.

Topic:

ALLSTaR - Automated LLM-Driven Scheduler Generation and Testing for Intent-Based RAN 

Abstract:

The evolution toward open, programmable O-RAN and AI-RAN 6G networks creates unprecedented opportunities for Intent-Based Networking (IBN) to dynamically optimize RAN operations based on dynamic operators requirements. However, applying IBN effectively to the RANscheduler - a critical component determining resource allocation and system performance - remains a significant challenge. Current approaches predominantly rely on coarse-grained network slicing, lacking the granularity for dynamic adaptation to individual user conditions and traffic patterns. Despite the existence of a vast body of scheduling algorithms that could potentially translate high-level intents into executable policies, their practical utilization is hindered by implementation heterogeneity, insufficient systematic evaluation in production environments, and the complexity of developing high-performance scheduler implementations. This necessitates a more granular, flexible, and verifiable approach to align scheduler behavior with operator-defined intents.

To address these limitations, we propose ALLSTaR, a novel framework leveraging LLMs for automated, intent-driven scheduler design, implementation, and evaluation. ALLSTaR interprets natural language intents, automatically generates functional scheduler code from the research literature using Optical Character Recognition (OCR) and LLMs, and intelligently matches operator intents to the most suitable scheduler(s). Our implementation deploys these schedulers as O-RAN dApps, enabling on-the-fly deployment and comprehensive testing on a production-grade, multi-vendor 5G-compliant testbed. This approach has enabled the largest-scale OTA experimental comparison of 18 scheduling algorithms automatically synthesized from the academic literature. The resulting performance profiles serve as the input for our Intent-Based Scheduling framework, which  dynamically selects and deploys appropriate schedulers that optimally satisfy operator intents. We validate our approach through multiple use cases unattainable with current slicing-based optimization techniques, demonstrating fine-grained control based on buffer status, physical layer conditions, and heterogeneous traffic types.

Speaker:

Maxime Elkael is a Postodoctoral Researcher at the Institute for the Wireless Internet of Things at Northeastern University. He received his Ph.D in Computer Science from Institut Polytechnique De Paris/Telecom SudParis in 2023. His research interest lies at the intersection of optimization theory, artificial intelligence and graph theory applied to next generation wireless networks, especially Open RAN networks.